{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d885824b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a2494ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLORS = [\n",
    "    '#00B0F0',\n",
    "    '#FF0000'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fd06fd",
   "metadata": {},
   "source": [
    "# Chapter 03\n",
    "\n",
    "In this chapter we focus on building an intuition between causal concepts and linear regression. Next, we move to a comparison between observational and interventional distributions and show why and how they might be different. Finally, we introduce the concept of structural models. Structural models will naturally lead us to the concept of graphical models in the next chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b582f3",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d5abac",
   "metadata": {},
   "source": [
    "Let's build a model according to the followingh specification:\n",
    "\n",
    "$$\\Large \\hat{y}_i = 1.12 + 0.93x_i + 0.5 \\epsilon_i$$ \n",
    "\n",
    "where $\\epsilon \\sim \\mathcal{N}(0, 1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b015b995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.11530002]\n",
      " [ 1.         -0.43617719]\n",
      " [ 1.         -0.54138887]\n",
      " [ 1.         -1.64773122]\n",
      " [ 1.         -0.32616934]]\n"
     ]
    }
   ],
   "source": [
    "# Set the seed for reproducibility\n",
    "np.random.seed(45)\n",
    "\n",
    "# No. of samples\n",
    "N_SAMPLES = 5000\n",
    "\n",
    "# Define true model parameters\n",
    "alpha = 1.12\n",
    "beta = 0.93\n",
    "epsilon = np.random.randn(N_SAMPLES)\n",
    "\n",
    "# Generate X\n",
    "X = np.random.randn(N_SAMPLES)\n",
    "\n",
    "# Compute Y\n",
    "y = alpha + beta * X + 0.5 * epsilon\n",
    "\n",
    "# Statsmodel models require us to add constant\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "print(X[:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef7a7087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.771\n",
      "Model:                            OLS   Adj. R-squared:                  0.771\n",
      "Method:                 Least Squares   F-statistic:                 1.681e+04\n",
      "Date:                Sun, 05 Jun 2022   Prob (F-statistic):               0.00\n",
      "Time:                        16:44:06   Log-Likelihood:                -3615.0\n",
      "No. Observations:                5000   AIC:                             7234.\n",
      "Df Residuals:                    4998   BIC:                             7247.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.1243      0.007    159.391      0.000       1.110       1.138\n",
      "x1             0.9212      0.007    129.669      0.000       0.907       0.935\n",
      "==============================================================================\n",
      "Omnibus:                        0.129   Durbin-Watson:                   1.986\n",
      "Prob(Omnibus):                  0.938   Jarque-Bera (JB):                0.163\n",
      "Skew:                           0.002   Prob(JB):                        0.922\n",
      "Kurtosis:                       2.972   Cond. No.                         1.02\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the model and fit it\n",
    "model = sm.OLS(y, X)\n",
    "fitted_model = model.fit()\n",
    "\n",
    "# Print results summary\n",
    "print(fitted_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16d214bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "y_pred = fitted_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89d08e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X[:, 1], y, color=COLORS[0], label='Observed', alpha=.2)\n",
    "plt.plot(X[:, 1], y_pred, color=COLORS[1], label='Fitted', alpha=.8)\n",
    "plt.xlabel('$X$', alpha=.5)\n",
    "plt.ylabel('$Y$', alpha=.5)\n",
    "legend = plt.legend()\n",
    "\n",
    "# Set opacity for the legend\n",
    "[l.set_alpha(.8) for l in legend.legendHandles]\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232ecda8",
   "metadata": {},
   "source": [
    "### Reversed model\n",
    "\n",
    "Now we're going to reverse the ordering of variables and regress $X$ on $Y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f95e3192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's remove the constant (now X becomes our target variable) \n",
    "x_rev = X[:, 1]\n",
    "\n",
    "# Let's add constant (now Y becomes our predictor)\n",
    "Y_rev = sm.add_constant(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b7190193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.771\n",
      "Model:                            OLS   Adj. R-squared:                  0.771\n",
      "Method:                 Least Squares   F-statistic:                 1.681e+04\n",
      "Date:                Sun, 05 Jun 2022   Prob (F-statistic):               0.00\n",
      "Time:                        19:16:00   Log-Likelihood:                -3375.0\n",
      "No. Observations:                5000   AIC:                             6754.\n",
      "Df Residuals:                    4998   BIC:                             6767.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.9441      0.010    -96.048      0.000      -0.963      -0.925\n",
      "x1             0.8368      0.006    129.669      0.000       0.824       0.849\n",
      "==============================================================================\n",
      "Omnibus:                        0.590   Durbin-Watson:                   1.994\n",
      "Prob(Omnibus):                  0.745   Jarque-Bera (JB):                0.582\n",
      "Skew:                          -0.026   Prob(JB):                        0.748\n",
      "Kurtosis:                       3.003   Cond. No.                         2.83\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the model and fit it\n",
    "model_rev = sm.OLS(x_rev, Y_rev)\n",
    "fitted_model_rev = model_rev.fit()\n",
    "\n",
    "# Print results summary\n",
    "print(fitted_model_rev.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "060e18e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "x_pred_rev = fitted_model_rev.predict(Y_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4b02119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(Y_rev[:, 1], x_rev, color=COLORS[0], label='Observed', alpha=.2)\n",
    "plt.plot(Y_rev[:, 1], x_pred_rev, color=COLORS[1], label='Fitted', alpha=.8)\n",
    "plt.xlabel('$Y$', alpha=.5)\n",
    "plt.ylabel('$X$', alpha=.5)\n",
    "legend = plt.legend()\n",
    "\n",
    "# Set opacity for the legend\n",
    "[l.set_alpha(.8) for l in legend.legendHandles]\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af952457",
   "metadata": {},
   "source": [
    "## Should we always control for all available covariates?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c726a994",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8029c0e",
   "metadata": {},
   "source": [
    "#### Visualize the model\n",
    "\n",
    "To correctly render the graphs you need to install graphviz backend. \n",
    "\n",
    "More details: https://pypi.org/project/graphviz/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e649176f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5c72ad80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a directed graph\n",
    "g_1 = graphviz.Digraph(format='png')\n",
    "\n",
    "# Add nodes\n",
    "nodes_1 = ['A', 'X', 'B', 'Y']\n",
    "[g_1.node(n) for n in nodes_1]\n",
    "\n",
    "g_1.edges(['AX', 'XB', 'AY', 'YB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f9db38a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'img\\\\ch_03_graph_01.png'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Render for print\n",
    "g_1.render('img/ch_03_graph_01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c0d2a868",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "# Display the graph\n",
    "g_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "43b7db4f",
   "metadata": {},
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    "# Set random seed for reproducibility\n",
    "np.random.seed(45)\n",
    "\n",
    "# Define the sample size\n",
    "N_SAMPLES = 1000\n",
    "\n",
    "# Build the graph (note that the coefficients are arbitrarily chosen)\n",
    "a = np.random.randn(N_SAMPLES)\n",
    "x = 2 * a + 0.5 * np.random.randn(N_SAMPLES)\n",
    "y = 2 * a + 0.5 * np.random.randn(N_SAMPLES)\n",
    "b = 1.5 * x + 0.75 * y"
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   "cell_type": "code",
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   "id": "127e8193",
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      "Params: [-0.02   0.947]\n",
      "p-vals: [0.359 0.   ]\n",
      "Signif: [False  True]\n",
      "\n",
      "Params: [-0.011  0.014  1.967]\n",
      "p-vals: [0.488 0.657 0.   ]\n",
      "Signif: [False False  True]\n",
      "\n",
      "Params: [-0.    -2.     1.333]\n",
      "p-vals: [0.133 0.    0.   ]\n",
      "Signif: [False  True  True]\n",
      "\n",
      "Params: [ 0.    -2.     0.     1.333]\n",
      "p-vals: [0. 0. 0. 0.]\n",
      "Signif: [ True  True  True  True]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define four model variants\n",
    "variants = [\n",
    "    [x],\n",
    "    [x, a],\n",
    "    [x, b],\n",
    "    [x, a, b]\n",
    "]\n",
    "\n",
    "# Fit models iteratively and store the results\n",
    "results = []\n",
    "for variant in variants:\n",
    "    X = sm.add_constant(np.stack(variant).T)\n",
    "    \n",
    "    # Instantiate the model and fit it\n",
    "    model = sm.OLS(y, X)\n",
    "    fitted_model = model.fit()\n",
    "    \n",
    "    results.append((fitted_model.params, fitted_model.pvalues))\n",
    "    \n",
    "    print(f'Params: {fitted_model.params.round(3)}')\n",
    "    print(f'p-vals: {fitted_model.pvalues.round(3)}')\n",
    "    print(f'Signif: {fitted_model.pvalues <= .05}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4a521035",
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.882\n",
      "Model:                            OLS   Adj. R-squared:                  0.882\n",
      "Method:                 Least Squares   F-statistic:                     7477.\n",
      "Date:                Thu, 09 Jun 2022   Prob (F-statistic):               0.00\n",
      "Time:                        19:40:09   Log-Likelihood:                -1062.2\n",
      "No. Observations:                1000   AIC:                             2128.\n",
      "Df Residuals:                     998   BIC:                             2138.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0204      0.022     -0.918      0.359      -0.064       0.023\n",
      "x1             0.9472      0.011     86.468      0.000       0.926       0.969\n",
      "==============================================================================\n",
      "Omnibus:                        4.922   Durbin-Watson:                   1.870\n",
      "Prob(Omnibus):                  0.085   Jarque-Bera (JB):                3.922\n",
      "Skew:                           0.038   Prob(JB):                        0.141\n",
      "Kurtosis:                       2.703   Cond. No.                         2.02\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the model and fit it\n",
    "model = sm.OLS(y, X)\n",
    "fitted_model = model.fit()\n",
    "\n",
    "# Print results summary\n",
    "print(fitted_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8a3dd1",
   "metadata": {},
   "source": [
    "## Regression and structural models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c104c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a directed graph\n",
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    "    format='png',\n",
    "    graph_attr={\n",
    "        'rankdir':'LR', \n",
    "        'ranksep': '.6',\n",
    "        'nodesep': '1.5'\n",
    "    })\n",
    "\n",
    "# Add nodes\n",
    "nodes_2 = ['A', 'X', 'Y']\n",
    "[g_2.node(n) for n in nodes_2]\n",
    "\n",
    "g_2.edges(['AY', 'AX', 'XY'])"
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   "id": "93ef751e",
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    "g_2"
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     },
     "execution_count": 147,
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    "# Render for print\n",
    "g_2.render('img/ch_03_graph_02')"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "276c0a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seed for reproducibility\n",
    "np.random.seed(45)\n",
    "\n",
    "# Define the sample size\n",
    "N_SAMPLES = 1000\n",
    "\n",
    "# Define the SCM\n",
    "a = np.random.randn(N_SAMPLES)\n",
    "x = 2 * a + .7 * np.random.randn(N_SAMPLES)\n",
    "y = 2 * a + 3 * x + .75 * x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b428ebcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 3.294e+32\n",
      "Date:                Sun, 05 Mar 2023   Prob (F-statistic):               0.00\n",
      "Time:                        12:51:10   Log-Likelihood:                 30899.\n",
      "No. Observations:                1000   AIC:                        -6.179e+04\n",
      "Df Residuals:                     996   BIC:                        -6.177e+04\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       -2.22e-16   3.57e-16     -0.622      0.534   -9.23e-16    4.79e-16\n",
      "x              3.0000   4.14e-16   7.25e+15      0.000       3.000       3.000\n",
      "x^2            0.7500   4.78e-17   1.57e+16      0.000       0.750       0.750\n",
      "a              2.0000   8.67e-16   2.31e+15      0.000       2.000       2.000\n",
      "==============================================================================\n",
      "Omnibus:                        0.858   Durbin-Watson:                   1.912\n",
      "Prob(Omnibus):                  0.651   Jarque-Bera (JB):                0.763\n",
      "Skew:                          -0.062   Prob(JB):                        0.683\n",
      "Kurtosis:                       3.054   Cond. No.                         24.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Statsmodel models require us to add constant\n",
    "X = sm.add_constant(np.stack([x, x**2, a]).T)\n",
    "\n",
    "# Instantiate the model and fit it\n",
    "model = sm.OLS(y, X)\n",
    "fitted_model = model.fit()\n",
    "\n",
    "# Print results summary\n",
    "print(fitted_model.summary(xname=['const', 'x', 'x^2', 'a']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40bd3c3d",
   "metadata": {},
   "outputs": [],
   "source": []
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